Very large fMRI study using the IMAGEN database

• Verfasst von: Thyreau, Benjamin [VerfasserIn]
• Verfasst von: Vollstädt-Klein, Sabine [VerfasserIn]
• Titel: Very large fMRI study using the IMAGEN database
• Titelzusatz: sensitivity-specificity and population effect modeling in relation to the underlying anatomy
• Verf.angabe: Benjamin Thyreau, Yannick Schwartz, Bertrand Thirion, Vincent Frouin, Eva Loth, Sabine Vollstädt-Klein, Tomas Paus, Eric Artiges, Patricia J. Conrod, Gunter Schumann, Robert Whelan, Jean-Baptiste Poline and The IMAGEN Consortium
• E-Jahr: 2012
• Jahr: 15 May 2012
• Umfang: 9 S.
• Fussnoten: Gesehen am 30.04.2018
• Titel Quelle: Enthalten in: NeuroImage
• Ort Quelle: Orlando, Fla. : Academic Press, 1992
• Jahr Quelle: 2012
• Band/Heft Quelle: 61(2012), 1, Seite 295-303
• ISSN Quelle: 1095-9572
• DOI: doi:10.1016/j.neuroimage.2012.02.083
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: Brain mapping: methods
• Sach-SW: Databasing
• Sach-SW: Likelihood functions
• Sach-SW: Linear models
• Sach-SW: Magnetic resonance imaging
• Sach-SW: Sensitivity and specificity
• K10plus-PPN: 1572489014

Abstract

In this paper we investigate the use of classical fMRI Random Effect (RFX) group statistics when analyzing a very large cohort and the possible improvement brought from anatomical information. Using 1326 subjects from the IMAGEN study, we first give a global picture of the evolution of the group effect t-value from a simple face-watching contrast with increasing cohort size. We obtain a wide activated pattern, far from being limited to the reasonably expected brain areas, illustrating the difference between statistical significance and practical significance. This motivates us to inject tissue-probability information into the group estimation, we model the BOLD contrast using a matter-weighted mixture of Gaussians and compare it to the common, single-Gaussian model. In both cases, the model parameters are estimated per-voxel for one subgroup, and the likelihood of both models is computed on a second, separate subgroup to reflect model generalization capacity. Various group sizes are tested, and significance is asserted using a 10-fold cross-validation scheme. We conclude that adding matter information consistently improves the quantitative analysis of BOLD responses in some areas of the brain, particularly those where accurate inter-subject registration remains challenging.